Surrogate Model Based Optimization Design Method Research For Gear Transmission Components Of HEV

Hybrid electric vehicle (HEV) an important way to solve the carbon emissions,which combine use primary and auxiliary energy with high fuel efficiency, lowemissions, strong power and good battery life, gradually gets more and more attention.This study does a research of parameter optimization methods of drive gear in thehybrid electric vehicle power split device (PSD), hoping to get better performance andmore lightweight of the transmission gear through the optimization of under thehybrid electric vehicle operating conditions.Based on the National Natural Science Foundation of China (51075179), thisstudy does a research of gear drive and optimization methods for PSD, analyzed theeffects of temperature on the ring gears and gears parameters optimization. The mainresearch work includes the following:Firstly, the background of hybrid vehicles was introduced in detail, the study ofgear optimization methods were introduced by means of introduce the research statusof the drive gear in domestic and foreign. Conditions of HEV were obtained by usingadvisor. Based on this condition, thermal-elastic coupling analysis was carry out of apair of transmission gears for planetary gear train. Temperature field distribution andthe stress field distribution were obtained during the drive teeth. The results of theinfluence of temperature rise on the tooth surface on the tooth surface contact stresswere also generated.For the phenomenon of shock, vibration, noise, tooth surface of partial load andthe contact stress too much of tooth gear during tooth transmission process, toothprofile and tooth practice to fix the drum were carried out. Practice method wascharacterized by analyzing the results of cultivation method were suitable for bevelgears. The analysis showed that the practice gear can improve gear efficiency, toothpartial load and reduce noise and vibration and so on. Surrogate model-based technique was proposed to optimize the transmissiongear parameters for solving multi-objective optimization question. Using of optimallatin hypercube experimental design methods obtain sample points and RBF modelwas used to established surrogate model. The accuracy test was used to prove thefeasibility of surrogate model can be used in gear optimization. Contrast NSGA-II andNSGA-II-NLPQL, the optimal solution proved to be better.